IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i8p2805-2818.html
   My bibliography  Save this article

Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads

Author

Listed:
  • Jenq-Tzong Shiau
  • Ting-Ju Chen

Abstract

Assessing suspended sediment loads in rivers is important since it affects water quality, hydraulic-facility design, and many other sediment-induced problems. Sediment-load estimation heavily depends upon empirical approaches such as a sediment rating curve, which is the empirical relationship between sediment load and river discharge. However, the sediment rating curve is insufficient to describe the inevitable scatter between sediment and discharge. This study aims to develop a probabilistic estimation scheme for daily and annual suspended sediment loads using quantile regression. All recorded daily suspended sediment load and discharge data are employed to construct quantile-dependent sediment rating curves. The empirical probability distribution of daily suspended sediment load is then built by integrating the conditional estimations associated with the corresponding quantiles for a given discharge. The probability distribution of a cumulative sediment load over a longer period can also be derived by the obtained daily sediment-load probability distributions and convolution theorem. The proposed approach is applied to the Laonung station located in southern Taiwan. The results indicate that the proposed approach provides not only the probabilistic description for daily and annual suspended sediment loads, but also the single estimations including the mean, median, and mode of the derived probability distribution. For the 1,110 recorded data of Laonung station during the 1959–2008 period, the proposed mean and median estimation schemes outperform the traditional sediment-rating-curve approach for less mean absolute errors. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Jenq-Tzong Shiau & Ting-Ju Chen, 2015. "Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2805-2818, June.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:8:p:2805-2818
    DOI: 10.1007/s11269-015-0971-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-0971-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-0971-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, November.
    3. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Vlassios Hrissanthou, 2014. "Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3727-3743, September.
    4. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
    5. Aytac Guven & Özgür Kişi, 2011. "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 691-704, January.
    6. Alagidede, Paul & Panagiotidis, Theodore, 2012. "Stock returns and inflation: Evidence from quantile regressions," Economics Letters, Elsevier, vol. 117(1), pages 283-286.
    7. Raveendra Rai & B. Mathur, 2008. "Event-based Sediment Yield Modeling using Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 423-441, April.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jenq-Tzong Shiau & Jia-Wei Lin, 2016. "Clustering Quantile Regression-Based Drought Trends in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1053-1069, February.
    2. Xiuyu Liang & Keith Schilling & You-Kuan Zhang & Christopher Jones, 2016. "Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1771-1784, March.
    3. Hai Tao & Behrooz Keshtegar & Zaher Mundher Yaseen, 2019. "The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4471-4490, October.
    4. Xiuyu Liang & Keith Schilling & You-Kuan Zhang & Christopher Jones, 2016. "Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1771-1784, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    2. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
    3. Badshah, Ihsan & Frijns, Bart & Knif, Johan & Tourani-Rad, Alireza, 2016. "Asymmetries of the intraday return-volatility relation," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 182-192.
    4. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    5. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    6. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
    7. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    8. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    9. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    10. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    11. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    12. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    13. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    14. Chesher, Andrew, 2017. "Understanding the effect of measurement error on quantile regressions," Journal of Econometrics, Elsevier, vol. 200(2), pages 223-237.
    15. Bampinas, Georgios & Panagiotidis, Theodore, 2016. "Hedging inflation with individual US stocks: A long-run portfolio analysis," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 374-392.
    16. Kleopatra Nikolaou, 2007. "The behaviour of the real exchange rate: Evidence from regression quantiles," Money Macro and Finance (MMF) Research Group Conference 2006 46, Money Macro and Finance Research Group.
    17. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    18. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    19. Jean-Marc Fournier & Isabell Koske, 2012. "The determinants of earnings inequality: evidence from quantile regressions," OECD Journal: Economic Studies, OECD Publishing, vol. 2012(1), pages 7-36.
    20. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:8:p:2805-2818. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.